CN110801203B - Human cranial nerve fiber tracking method based on local features - Google Patents
Human cranial nerve fiber tracking method based on local features Download PDFInfo
- Publication number
- CN110801203B CN110801203B CN201911043684.2A CN201911043684A CN110801203B CN 110801203 B CN110801203 B CN 110801203B CN 201911043684 A CN201911043684 A CN 201911043684A CN 110801203 B CN110801203 B CN 110801203B
- Authority
- CN
- China
- Prior art keywords
- interpolation
- tensor
- grid
- human brain
- points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
- A61B5/4064—Evaluating the brain
Abstract
The invention discloses a human brain nerve fiber tracking method based on local features, which is used for solving the problem that the periphery of a singular point cannot be correctly tracked in the human brain nerve fiber tracking process. The method mainly comprises the following steps: the method comprises the following steps of firstly, finding all grids with singular points from a human brain tensor field by using a singular point positioning method; step two, judging whether the midpoint of the grid falls on four quadrant angle bisectors or the center, if so, removing the side corresponding to the tensor with smaller anisotropy during the interpolation of the feature vector, and otherwise, removing the side with the farthest distance; thirdly, tensor field interpolation is carried out, the characteristic value of the midpoint of the grid is calculated by utilizing a scalar bilinear interpolation method, and the characteristic vector of the midpoint of the grid is calculated by utilizing tensor interpolation; and step four, rendering an interpolation result. The human brain nerve fiber tracking method can accurately find singular points in the human brain tensor field and overcome the problem of inaccurate tracking around the singular points, thereby improving the tracking accuracy.
Description
Technical Field
The invention relates to the technical field of human cranial nerve fiber tracking, in particular to a method for solving the problem that the periphery of a singular point cannot be correctly tracked in the human cranial nerve fiber tracking process.
Background
There are many nerve fibers in the human brain, and these intricate neurons are interwoven together to form a network. The network of neurons controls different levels of consciousness, movement, sleep, etc., for example, the human brain coordinates our perception, thinking and movement through its neuronal activity. Therefore, the tracking of the human brain nerve fibers can help human brain researchers draw detailed human brain nerve circuit diagrams, and further promotes the cognition of neuroscientists on the human brain. However, the human brain nerve fiber tracking is not ideal because of the large quantity, complexity and randomness of movement of the human brain nerve fiber.
At present, the tracking of human cranial nerve fibers mainly adopts methods such as electric probes, chemical reagents, genetic modification, tensor field interpolation and the like. Mathematically, the human brain can be represented as a diffusion tensor field, and the neurons are tensors in the tensor field, so that the tracking of the human brain nerve fibers is the tracking of flow lines in the tensor field. In general, a motion trajectory can be accurately tracked by a tensor field interpolation method, but when there is a tensor point (singular point) where the anisotropy is zero, the brownian motion has almost no direction around the singular point. It is difficult for the prior art to accurately track around singular points.
Therefore, there is a need for a method for neuroscientists to accurately find singular points in the human brain tensor field and to overcome the problem of inaccurate tracking around the singular points.
Disclosure of Invention
Aiming at the prior art, the invention provides a human cranial nerve fiber tracking method based on local characteristics, which solves the problem that the periphery of a singular point cannot be accurately tracked in the prior human cranial nerve fiber tracking technology.
In order to solve the technical problem, the invention provides a human cranial nerve fiber tracking method based on local features, which comprises the following steps:
the method comprises the following steps of firstly, finding all grids with singular points from a human brain tensor field by using a singular point positioning method;
step two, judging whether singular points in the grid are located on four quadrant angle bisectors or centers, if so, removing the edges corresponding to the smaller tensor of the anisotropic FA during the interpolation of the eigenvector, and otherwise, removing the edges with the farthest distance;
thirdly, tensor field interpolation, namely calculating an eigenvalue lambda of an interpolation point in the grid by using a scalar bilinear interpolation method, and calculating an eigenvector e of the interpolation point in the grid by using tensor interpolation;
and step four, rendering an interpolation result.
Further, in the first step of the method of the present invention, the singular point positioning method comprises the following steps:
firstly, determining the principal directions of four vertexes of a grid, wherein an eigenvector corresponding to the maximum eigenvalue is the principal direction of the tensor; then judging whether cos alpha of the tensor rotation angle of any two adjacent vertexes is smaller than zero or not; and finally, calculating the number n of cos alpha smaller than zero, wherein if n is an odd number, singular points exist in the grid, and otherwise, the singular points do not exist.
In step two of the method of the present invention, the expression of the anisotropy FA is as follows:
In the third step of the method of the present invention, the eigenvalue λ of the interpolation point in the grid is expressed as follows:
wherein the content of the first and second substances,andis R1And R2Eigenvalues of the two tensor points.
In the third step of the method of the invention, the feature vector e of the interpolation point in the grid is expressed as follows:
wherein the coefficient SyIs composed of Are each R1And R2The feature vectors of the two points are,is a rotation matrix.
Compared with the prior art, the invention has the beneficial effects that:
first, the present invention provides a singular point locating method in step one, by which all grids with singular points can be accurately found in the human brain tensor field.
Secondly, the invention provides a method for eliminating the interference of singular points in the tensor field to the tracking in the step two, and the problem that the singular points can not be accurately tracked around is solved.
Thirdly, during the interpolation of the tensor field in the third step, the eigenvalue and the eigenvector are respectively interpolated, and as a result, the anisotropy of the tensor field is kept as much as possible.
In conclusion, the invention provides a method for solving the problem that the periphery of a singular point cannot be correctly tracked in the process of tracking the human cranial nerve fibers. The method improves the accuracy of tracking the human cranial nerve fibers and further promotes the cognition of human cranial nerve circuits by human cranial researchers.
Drawings
FIG. 1 is a flow chart of the human cranial nerve fiber tracking method based on local features according to the present invention.
FIG. 2 is a schematic diagram of locating singular points in the present invention.
Figure 3 is a schematic diagram of tensor field interpolation in the present invention.
FIG. 4 is a partial code diagram of eigenvalue interpolation in the present invention.
FIG. 5 is a partial code diagram of feature vector interpolation in the present invention.
FIG. 6 is a diagram of the present invention in which a point falls on a first bisector of the quadrant angle, and the left part of the code is removed by comparing the anisotropy magnitudes of the corresponding two vertex tensors.
FIG. 7 is a graph showing the tracking effect of the present invention in the vicinity of the triple singular point.
FIG. 8 is a graph showing the tracking effect of the present invention in the vicinity of the wedge singularity.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, which are not intended to limit the invention in any way.
The invention provides a human brain nerve fiber tracking method based on local features, which mainly comprises the following steps: finding all grids with singular points from a human brain tensor field by using a singular point positioning method; judging whether singular points in the grid fall on four quadrant angle bisectors or centers, if so, removing the side corresponding to the smaller tensor of the anisotropy FA during the interpolation of the eigenvector, and otherwise, removing the side with the farthest distance; tensor field interpolation, namely calculating an eigenvalue lambda of an interpolation point in a grid by utilizing a scalar bilinear interpolation method, and calculating an eigenvector e of the interpolation point in the grid by utilizing tensor interpolation; and rendering an interpolation result, accurately tracking the nerve fibers near the singular point in the human brain tensor field, and simultaneously distributing the nerve fibers around the singular point in a uniform and accurate distribution mode.
As shown in fig. 1, the specific process is as follows:
in data representation, a singular point is a tensor with zero anisotropy, which has two or more equal eigenvalues. For one tensor, the eigenvector e generally represents the direction of the tensor, and its magnitude is represented by the eigenvalue λ, so the eigenvector corresponding to the largest eigenvalue is the principal direction of the tensor. Since brownian motion has almost no direction of motion around a singular point, it is difficult to accurately track around the singular point.
The singular point positioning method comprises the following steps: firstly, determining the principal directions of four vertexes of a grid, wherein an eigenvector corresponding to the maximum eigenvalue is the principal direction of the tensor; then, whether cos alpha of the tensor rotation angle of any two adjacent vertexes is smaller than zero is judged. And finally, calculating the number n of cos alpha smaller than zero, wherein if n is an odd number, singular points exist in the grid, and otherwise, the singular points do not exist.
FIG. 2 is a schematic diagram of locating singular points in the present invention. The four vertices of each rectangle in the figure are the four tensor samples, with the arrows pointing in the principal direction of the tensor. The principal direction rotation angle cos α of the two vertex tensors corresponding to the thick edges in the graph is smaller than zero. If a is a thick edge and is an odd number, singular points exist in the grid; and b, if two thick edges are even numbers, singular points do not exist in the grid.
Anisotropy is an important indicator of the study of the tensor field. At present, various anisotropy calculation methods exist, such as anisotropy index, fractional anisotropy, relative anisotropy and volume fraction, etc., however, the present invention calculates tensor anisotropy using the fractional anisotropy calculation method. The calculation formula of the anisotropy FA, i.e.
There is a subtle difference in the comparison of anisotropy when the tensor points in the grid fall on the four quadrant bisector and center. When the tensor point falls on the four-quadrant angular bisector, comparing the anisotropy of the tensors corresponding to the two vertexes; when the tensor point falls on the center, the sum of the two tensor anisotropies for each edge should be calculated and then the magnitudes are compared. FIG. 6 is a partial code diagram of the invention with dots falling on the first quadrant bisector and the left removed by comparing the magnitude of anisotropy.
And 105, carrying out tensor field interpolation, calculating an eigenvalue lambda of an interpolation point in the grid by using a scalar bilinear interpolation method, and calculating an eigenvector e of the interpolation point in the grid by using tensor interpolation.
In order to preserve anisotropy of the tensor field as much as possible, the tensor field interpolation separates eigenvalues and eigenvectors, and calculates by a scalar bilinear interpolation method as shown in fig. 4 and a tensor interpolation method as shown in fig. 5, respectively. The eigenvalues λ of the interpolation points, R as shown in FIG. 31And R2Are two dots, R1Characteristic value of pointNamely, it is
The characteristic value λ of the point P, i.e.
The feature vector of the interpolation point is e. As shown in FIG. 3, first R1Characteristics of the dots(Vector)Namely, it is
Then the feature vector e of point P, i.e.
And step 106, rendering an interpolation result.
Two types of singular points mainly exist in a human brain tensor field, which are a trisection (Trisector) and a wedge (wedge), respectively, and the streamline is difficult to accurately track near the two types of singular points by using a plurality of existing tracking technologies, and the tracking results always have the problems of infinite closeness, intersection and the like. To verify the effectiveness of our proposed local feature-based human cranial nerve fiber tracking method, we chose to track around these two singular points separately. Fig. 7 and 8 are graphs of the tracking effect in the vicinity of the triple point and the wedge singularity, respectively. The points circled by the boxes in the figure are seed points, i.e. the starting points of the tracing of each streamline. Obviously, the streamline traced by each seed point is uniformly distributed near the singular point, and the problems of infinite proximity, intersection and the like do not occur.
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative only and not restrictive, and various modifications which do not depart from the spirit of the present invention and which are intended to be covered by the claims of the present invention may be made by those skilled in the art.
Claims (4)
1. A human brain nerve fiber tracking method based on local features is characterized by comprising the following steps:
the method comprises the following steps of firstly, finding all grids with singular points from a human brain tensor field by using a singular point positioning method; the method comprises the following specific steps: firstly, determining the principal directions of four vertexes of a grid, wherein an eigenvector corresponding to the maximum eigenvalue is the principal direction of the tensor; then judging whether cos alpha of the tensor rotation angle of any two adjacent vertexes is smaller than zero or not; finally, calculating the number n of cos alpha smaller than zero, wherein if n is an odd number, singular points exist in the grid, otherwise, the singular points do not exist;
step two, judging whether singular points in the grid are located on four quadrant angle bisectors or centers, if so, removing the edges corresponding to the smaller tensor of the anisotropic FA during the interpolation of the eigenvector, and otherwise, removing the edges with the farthest distance;
thirdly, tensor field interpolation, namely calculating an eigenvalue lambda of an interpolation point in the grid by using a scalar bilinear interpolation method, and calculating an eigenvector e of the interpolation point in the grid by using tensor interpolation;
and step four, rendering an interpolation result.
3. The method for tracking human brain nerve fibers based on local features according to claim 1, wherein in the third step, the feature value λ of the interpolation points in the grid is expressed as follows:
4. The method for tracking human brain nerve fibers based on local features according to claim 1, wherein in the third step, the feature vector e of the interpolation points in the grid is represented as follows:
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911043684.2A CN110801203B (en) | 2019-10-30 | 2019-10-30 | Human cranial nerve fiber tracking method based on local features |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911043684.2A CN110801203B (en) | 2019-10-30 | 2019-10-30 | Human cranial nerve fiber tracking method based on local features |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110801203A CN110801203A (en) | 2020-02-18 |
CN110801203B true CN110801203B (en) | 2022-02-15 |
Family
ID=69489752
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201911043684.2A Active CN110801203B (en) | 2019-10-30 | 2019-10-30 | Human cranial nerve fiber tracking method based on local features |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110801203B (en) |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002017707A (en) * | 2000-07-05 | 2002-01-22 | Ge Yokogawa Medical Systems Ltd | Image pickup face determining method and mri device |
WO2006106242A1 (en) * | 2005-04-07 | 2006-10-12 | Inria Institut National De Recherche En Informatique Et En Automatique | Improved device for processing raw images or tensor images |
CN101833790A (en) * | 2010-04-30 | 2010-09-15 | 浙江大学 | Method for generating anisotropic quadrilateral grid based on wave equations |
CN103049901A (en) * | 2012-08-03 | 2013-04-17 | 上海理工大学 | Magnetic resonance diffusion tensor imaging fiber bundle tracking device |
CN103700146A (en) * | 2013-12-01 | 2014-04-02 | 北京航空航天大学 | Three-dimensional data visualization enhancing method based on anisotropic structure tensor |
CN103914431A (en) * | 2014-01-16 | 2014-07-09 | 同济大学 | Mesh-less method for calculating anisotropic structure radar cross section |
CN105842642A (en) * | 2016-03-17 | 2016-08-10 | 天津大学 | Fractional anisotropy microstructure characteristic extraction method based on kurtosis tensor and apparatus thereof |
CN110021003A (en) * | 2019-02-14 | 2019-07-16 | 清华大学 | Image processing method, image processing apparatus and magnetic resonance imaging device |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102005028475B4 (en) * | 2005-06-20 | 2008-04-03 | Siemens Ag | Method and device for determining coefficients of a magnetic resonance diffusion tensor |
US7480400B2 (en) * | 2006-03-16 | 2009-01-20 | Siemens Medical Solutions Usa, Inc. | Detection of fiber pathways |
-
2019
- 2019-10-30 CN CN201911043684.2A patent/CN110801203B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002017707A (en) * | 2000-07-05 | 2002-01-22 | Ge Yokogawa Medical Systems Ltd | Image pickup face determining method and mri device |
WO2006106242A1 (en) * | 2005-04-07 | 2006-10-12 | Inria Institut National De Recherche En Informatique Et En Automatique | Improved device for processing raw images or tensor images |
CN101833790A (en) * | 2010-04-30 | 2010-09-15 | 浙江大学 | Method for generating anisotropic quadrilateral grid based on wave equations |
CN103049901A (en) * | 2012-08-03 | 2013-04-17 | 上海理工大学 | Magnetic resonance diffusion tensor imaging fiber bundle tracking device |
CN103700146A (en) * | 2013-12-01 | 2014-04-02 | 北京航空航天大学 | Three-dimensional data visualization enhancing method based on anisotropic structure tensor |
CN103914431A (en) * | 2014-01-16 | 2014-07-09 | 同济大学 | Mesh-less method for calculating anisotropic structure radar cross section |
CN105842642A (en) * | 2016-03-17 | 2016-08-10 | 天津大学 | Fractional anisotropy microstructure characteristic extraction method based on kurtosis tensor and apparatus thereof |
CN110021003A (en) * | 2019-02-14 | 2019-07-16 | 清华大学 | Image processing method, image processing apparatus and magnetic resonance imaging device |
Non-Patent Citations (6)
Title |
---|
A survey on visualization of tensor field;Bi C, 等;《Journal of Visualization》;20190322;全文 * |
Collateral nerve fibers in human spinal cord: visualization with magnetic resonance diffusion tensor imaging;Mamata H, 等;《Neuroimage》;20060120;全文 * |
Interactive control of mesh topology in quadrilateral mesh generation based on 2D tensor fields;Bi C,等;《International Symposium on Visual Computing. Springer, Berlin, Heidelberg》;20121231;全文 * |
Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment;Snook L,等;《Neuroimage》;20070101;第34卷(第1期);全文 * |
基于k-medoids聚类的人脑DTI图像分割算法及其纤维追踪的研究;林涛;《中国优秀硕士学位论文全文数据库》;20170215(第2期);全文 * |
弥散张量成像在颅脑损伤昏迷病人中的应用;张晓峰,等;《国际神经病学神经外科学杂志》;20190430;第36卷(第4期);全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN110801203A (en) | 2020-02-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110335297B (en) | Point cloud registration method based on feature extraction | |
Bookstein et al. | A feature space for edgels in images with landmarks | |
CN102782723B (en) | Position and direction estimation method and equipment thereof | |
Gu et al. | GPU-based fast gamma index calculation | |
EP0313586A1 (en) | A method for global blending of computer modeled solid objects using a convolution integral | |
Kenobi et al. | Shape curves and geodesic modelling | |
CN109101741B (en) | Complex surface detection self-adaptive sampling method based on triangular mesh simplification | |
CN108416801B (en) | Har-SURF-RAN characteristic point matching method for stereoscopic vision three-dimensional reconstruction | |
CN109685841B (en) | Registration method and system of three-dimensional model and point cloud | |
Franke et al. | Least squares surface approximation to scattered data using multiquadratic functions | |
Rouhani et al. | Implicit polynomial representation through a fast fitting error estimation | |
CN111859825A (en) | Method and equipment for simulating unsteady non-pressure flow numerical value with arbitrary flow-solid interface | |
CN110801203B (en) | Human cranial nerve fiber tracking method based on local features | |
CN108364268A (en) | A kind of single frames bar graph phase recovery method and device | |
CN105389476B (en) | The interpolation algorithm of IMRT intended dose data based on Gradient Features | |
CN104700368A (en) | Self-adaptive sliding method of displacement field of digital image relevant method based on kernel function | |
CN113658194A (en) | Point cloud splicing method and device based on reference object and storage medium | |
TWI406189B (en) | Method for constructing triangular grids of point clouds | |
CN105387826A (en) | method and apparatus for quantifying dimensional variations and process capability | |
CN115147471A (en) | Laser point cloud automatic registration method based on curvature density characteristics | |
Kleban et al. | Finite-size effects at 2D Ising critical points via conformal mapping | |
US20140044332A1 (en) | Transformation method for diffusion spectrum imaging using large deformation diffeomorphic metric mapping | |
CN114022526A (en) | SAC-IA point cloud registration method based on three-dimensional shape context | |
Wang et al. | Brain surface conformal parameterization with the Ricci flow | |
Hoffmann et al. | Robustness of topological defects in discrete domains |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |